Overview

Dataset statistics

Number of variables16
Number of observations738944
Missing cells1103769
Missing cells (%)9.3%
Duplicate rows3762
Duplicate rows (%)0.5%
Total size in memory95.8 MiB
Average record size in memory136.0 B

Variable types

Numeric9
Text4
DateTime2
Categorical1

Alerts

Recovered has constant value "0.0"Constant
Dataset has 3762 (0.5%) duplicate rowsDuplicates
Active is highly overall correlated with Confirmed and 1 other fieldsHigh correlation
Confirmed is highly overall correlated with Active and 1 other fieldsHigh correlation
Deaths is highly overall correlated with Active and 1 other fieldsHigh correlation
FIPS has 137632 (18.6%) missing valuesMissing
Admin2 has 136896 (18.5%) missing valuesMissing
Province_State has 32936 (4.5%) missing valuesMissing
Lat has 16744 (2.3%) missing valuesMissing
Long_ has 16744 (2.3%) missing valuesMissing
Recovered has 738000 (99.9%) missing valuesMissing
Incident_Rate has 17163 (2.3%) missing valuesMissing
Case_Fatality_Ratio has 7654 (1.0%) missing valuesMissing
Confirmed is highly skewed (γ1 = 20.47479795)Skewed
Active is highly skewed (γ1 = 20.59259254)Skewed
Case_Fatality_Ratio is highly skewed (γ1 = 69.54895027)Skewed
Confirmed has 7870 (1.1%) zerosZeros
Deaths has 31811 (4.3%) zerosZeros
Case_Fatality_Ratio has 25604 (3.5%) zerosZeros
New_Cases has 16595 (2.2%) zerosZeros

Reproduction

Analysis started2025-11-22 15:51:53.042432
Analysis finished2025-11-22 15:52:39.700295
Duration46.66 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

FIPS
Real number (ℝ)

Missing 

Distinct3268
Distinct (%)0.5%
Missing137632
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean32405.943
Minimum60
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 MiB
2025-11-22T15:52:39.846455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile5095
Q119048.5
median30068
Q347041.5
95-th percentile56011
Maximum99999
Range99939
Interquartile range (IQR)27993

Descriptive statistics

Standard deviation18053.633
Coefficient of variation (CV)0.55710871
Kurtosis0.44724736
Mean32405.943
Median Absolute Deviation (MAD)12908
Skewness0.57375176
Sum1.9486083 × 1010
Variance3.2593368 × 108
MonotonicityNot monotonic
2025-11-22T15:52:39.991526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39151184
 
< 0.1%
39155184
 
< 0.1%
39157184
 
< 0.1%
90039184
 
< 0.1%
39159184
 
< 0.1%
39161184
 
< 0.1%
39163184
 
< 0.1%
39165184
 
< 0.1%
39167184
 
< 0.1%
39169184
 
< 0.1%
Other values (3258)599472
81.1%
(Missing)137632
 
18.6%
ValueCountFrequency (%)
60184
< 0.1%
66184
< 0.1%
69184
< 0.1%
78184
< 0.1%
1001184
< 0.1%
1003184
< 0.1%
1005184
< 0.1%
1007184
< 0.1%
1009184
< 0.1%
1011184
< 0.1%
ValueCountFrequency (%)
99999184
< 0.1%
90056184
< 0.1%
90055184
< 0.1%
90054184
< 0.1%
90053184
< 0.1%
90051184
< 0.1%
90050184
< 0.1%
90049184
< 0.1%
90048184
< 0.1%
90047184
< 0.1%

Admin2
Text

Missing 

Distinct1927
Distinct (%)0.3%
Missing136896
Missing (%)18.5%
Memory size11.3 MiB
2025-11-22T15:52:40.340644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length35
Mean length7.1451711
Min length3

Characters and Unicode

Total characters4301736
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOut of ME
2nd rowOut of ME
3rd rowOut of ME
4th rowOut of ME
5th rowOut of ME
ValueCountFrequency (%)
unassigned9384
 
1.4%
washington5520
 
0.8%
jefferson5152
 
0.8%
franklin4784
 
0.7%
st4784
 
0.7%
lincoln4416
 
0.7%
jackson4416
 
0.7%
san3864
 
0.6%
madison3680
 
0.6%
of3680
 
0.6%
Other values (1952)604992
92.4%
2025-11-22T15:52:40.827806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a430560
 
10.0%
e408664
 
9.5%
n365792
 
8.5%
o325864
 
7.6%
r285200
 
6.6%
l238464
 
5.5%
i229816
 
5.3%
s203504
 
4.7%
t191544
 
4.5%
u110032
 
2.6%
Other values (47)1512296
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4301736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a430560
 
10.0%
e408664
 
9.5%
n365792
 
8.5%
o325864
 
7.6%
r285200
 
6.6%
l238464
 
5.5%
i229816
 
5.3%
s203504
 
4.7%
t191544
 
4.5%
u110032
 
2.6%
Other values (47)1512296
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4301736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a430560
 
10.0%
e408664
 
9.5%
n365792
 
8.5%
o325864
 
7.6%
r285200
 
6.6%
l238464
 
5.5%
i229816
 
5.3%
s203504
 
4.7%
t191544
 
4.5%
u110032
 
2.6%
Other values (47)1512296
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4301736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a430560
 
10.0%
e408664
 
9.5%
n365792
 
8.5%
o325864
 
7.6%
r285200
 
6.6%
l238464
 
5.5%
i229816
 
5.3%
s203504
 
4.7%
t191544
 
4.5%
u110032
 
2.6%
Other values (47)1512296
35.2%

Province_State
Text

Missing 

Distinct598
Distinct (%)0.1%
Missing32936
Missing (%)4.5%
Memory size11.3 MiB
2025-11-22T15:52:41.107845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length32
Mean length8.5311441
Min length3

Characters and Unicode

Total characters6023056
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralian Capital Territory
2nd rowNew South Wales
3rd rowNorthern Territory
4th rowQueensland
5th rowSouth Australia
ValueCountFrequency (%)
texas46920
 
5.5%
virginia34960
 
4.1%
georgia29624
 
3.5%
north28704
 
3.4%
carolina27232
 
3.2%
new24656
 
2.9%
kentucky22264
 
2.6%
dakota22264
 
2.6%
missouri21528
 
2.5%
south21344
 
2.5%
Other values (662)576656
67.4%
2025-11-22T15:52:41.506628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a804080
13.4%
i599472
 
10.0%
n483920
 
8.0%
o472144
 
7.8%
s455216
 
7.6%
e362480
 
6.0%
r321080
 
5.3%
t216936
 
3.6%
l207920
 
3.5%
150144
 
2.5%
Other values (50)1949664
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)6023056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a804080
13.4%
i599472
 
10.0%
n483920
 
8.0%
o472144
 
7.8%
s455216
 
7.6%
e362480
 
6.0%
r321080
 
5.3%
t216936
 
3.6%
l207920
 
3.5%
150144
 
2.5%
Other values (50)1949664
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6023056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a804080
13.4%
i599472
 
10.0%
n483920
 
8.0%
o472144
 
7.8%
s455216
 
7.6%
e362480
 
6.0%
r321080
 
5.3%
t216936
 
3.6%
l207920
 
3.5%
150144
 
2.5%
Other values (50)1949664
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6023056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a804080
13.4%
i599472
 
10.0%
n483920
 
8.0%
o472144
 
7.8%
s455216
 
7.6%
e362480
 
6.0%
r321080
 
5.3%
t216936
 
3.6%
l207920
 
3.5%
150144
 
2.5%
Other values (50)1949664
32.4%
Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
2025-11-22T15:52:41.685434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length2
Mean length2.9138446
Min length2

Characters and Unicode

Total characters2153168
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
us603336
80.2%
russia15272
 
2.0%
japan9016
 
1.2%
india6808
 
0.9%
colombia6256
 
0.8%
china6256
 
0.8%
mexico6072
 
0.8%
ukraine5152
 
0.7%
brazil4968
 
0.7%
peru4784
 
0.6%
Other values (225)84824
 
11.3%
2025-11-22T15:52:41.969159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S616216
28.6%
U612720
28.5%
a148120
 
6.9%
i97704
 
4.5%
n77832
 
3.6%
e63664
 
3.0%
s48944
 
2.3%
r41400
 
1.9%
o38456
 
1.8%
l37904
 
1.8%
Other values (50)370208
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2153168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S616216
28.6%
U612720
28.5%
a148120
 
6.9%
i97704
 
4.5%
n77832
 
3.6%
e63664
 
3.0%
s48944
 
2.3%
r41400
 
1.9%
o38456
 
1.8%
l37904
 
1.8%
Other values (50)370208
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2153168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S616216
28.6%
U612720
28.5%
a148120
 
6.9%
i97704
 
4.5%
n77832
 
3.6%
e63664
 
3.0%
s48944
 
2.3%
r41400
 
1.9%
o38456
 
1.8%
l37904
 
1.8%
Other values (50)370208
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2153168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S616216
28.6%
U612720
28.5%
a148120
 
6.9%
i97704
 
4.5%
n77832
 
3.6%
e63664
 
3.0%
s48944
 
2.3%
r41400
 
1.9%
o38456
 
1.8%
l37904
 
1.8%
Other values (50)370208
17.2%
Distinct205
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
Minimum2020-08-04 02:27:56
Maximum2022-09-01 04:21:03
Invalid dates0
Invalid dates (%)0.0%
2025-11-22T15:52:42.094814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:42.234825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Lat
Real number (ℝ)

Missing 

Distinct4022
Distinct (%)0.6%
Missing16744
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean35.736183
Minimum-71.9499
Maximum71.7069
Zeros0
Zeros (%)0.0%
Negative23184
Negative (%)3.1%
Memory size11.3 MiB
2025-11-22T15:52:42.366783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-71.9499
5-th percentile9.3077
Q133.191535
median37.8957
Q342.176955
95-th percentile49.448196
Maximum71.7069
Range143.6568
Interquartile range (IQR)8.9854206

Descriptive statistics

Standard deviation13.439624
Coefficient of variation (CV)0.37607887
Kurtosis9.7997342
Mean35.736183
Median Absolute Deviation (MAD)4.4878388
Skewness-2.5003793
Sum25808672
Variance180.62348
MonotonicityNot monotonic
2025-11-22T15:52:42.501566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.9399368
 
< 0.1%
44.11630765184
 
< 0.1%
44.59040891184
 
< 0.1%
43.11363907184
 
< 0.1%
43.0022601184
 
< 0.1%
44.4976179184
 
< 0.1%
42.2689144184
 
< 0.1%
40.88320119184
 
< 0.1%
41.71579493184
 
< 0.1%
42.16852837184
 
< 0.1%
Other values (4012)720176
97.5%
(Missing)16744
 
2.3%
ValueCountFrequency (%)
-71.9499184
< 0.1%
-52.368184
< 0.1%
-51.7963184
< 0.1%
-45.9864184
< 0.1%
-42.8821184
< 0.1%
-41.9198184
< 0.1%
-40.9006184
< 0.1%
-40.231184
< 0.1%
-38.9489184
< 0.1%
-38.4161184
< 0.1%
ValueCountFrequency (%)
71.7069184
< 0.1%
70.2998184
< 0.1%
69.31479216184
< 0.1%
68.27557185184
< 0.1%
68.0000418127
< 0.1%
68.000041857
 
< 0.1%
67.1471631184
< 0.1%
67.04919196184
< 0.1%
66.941626184
< 0.1%
66.8309184
< 0.1%

Long_
Real number (ℝ)

Missing 

Distinct4075
Distinct (%)0.6%
Missing16744
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean-71.109728
Minimum-178.1165
Maximum178.065
Zeros0
Zeros (%)0.0%
Negative634800
Negative (%)85.9%
Memory size11.3 MiB
2025-11-22T15:52:42.652400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-178.1165
5-th percentile-116.47227
Q1-96.595639
median-86.717326
Q3-77.3579
95-th percentile73.121219
Maximum178.065
Range356.1815
Interquartile range (IQR)19.237739

Descriptive statistics

Standard deviation55.354465
Coefficient of variation (CV)-0.7784373
Kurtosis5.2135408
Mean-71.109728
Median Absolute Deviation (MAD)9.628321
Skewness2.3670982
Sum-51355446
Variance3064.1168
MonotonicityNot monotonic
2025-11-22T15:52:42.795938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-36.782368
 
< 0.1%
-71.5724368
 
< 0.1%
-70.812368
 
< 0.1%
-73.6536368
 
< 0.1%
-74.03368
 
< 0.1%
-36.9541368
 
< 0.1%
-75.5277368
 
< 0.1%
-72.3311368
 
< 0.1%
-101.7068368
 
< 0.1%
-72.1416368
 
< 0.1%
Other values (4065)718520
97.2%
(Missing)16744
 
2.3%
ValueCountFrequency (%)
-178.1165184
< 0.1%
-175.1982184
< 0.1%
-174.1596184
< 0.1%
-172.1046184
< 0.1%
-170.132184
< 0.1%
-169.8672184
< 0.1%
-168.734184
< 0.1%
-164.0353804184
< 0.1%
-163.396788357
 
< 0.1%
-163.3967883127
< 0.1%
ValueCountFrequency (%)
178.065184
< 0.1%
177.6493184
< 0.1%
174.886184
< 0.1%
171.1845184
< 0.1%
169.4900869184
< 0.1%
166.9592184
< 0.1%
166.9315184
< 0.1%
165.618042184
< 0.1%
160.1562184
< 0.1%
160.038381957
 
< 0.1%

Confirmed
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct132737
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132146.92
Minimum-3073
Maximum33572631
Zeros7870
Zeros (%)1.1%
Negative2
Negative (%)< 0.1%
Memory size11.3 MiB
2025-11-22T15:52:42.932367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3073
5-th percentile532
Q13119
median8807
Q338301.25
95-th percentile445952
Maximum33572631
Range33575704
Interquartile range (IQR)35182.25

Descriptive statistics

Standard deviation816837.97
Coefficient of variation (CV)6.1812865
Kurtosis578.54552
Mean132146.92
Median Absolute Deviation (MAD)7274.5
Skewness20.474798
Sum9.7649172 × 1010
Variance6.6722426 × 1011
MonotonicityNot monotonic
2025-11-22T15:52:43.071001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07870
 
1.1%
1409
 
0.1%
13387
 
0.1%
535272
 
< 0.1%
434259
 
< 0.1%
8257
 
< 0.1%
9248
 
< 0.1%
103243
 
< 0.1%
100236
 
< 0.1%
712232
 
< 0.1%
Other values (132727)728531
98.6%
ValueCountFrequency (%)
-30732
 
< 0.1%
07870
1.1%
1409
 
0.1%
225
 
< 0.1%
3133
 
< 0.1%
4232
 
< 0.1%
557
 
< 0.1%
637
 
< 0.1%
7129
 
< 0.1%
8257
 
< 0.1%
ValueCountFrequency (%)
335726311
 
< 0.1%
335533911
 
< 0.1%
335296691
 
< 0.1%
334939203
< 0.1%
334817901
 
< 0.1%
334619531
 
< 0.1%
334406641
 
< 0.1%
334116761
 
< 0.1%
333729743
< 0.1%
333578831
 
< 0.1%

Deaths
Real number (ℝ)

High correlation  Zeros 

Distinct20227
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1568.8279
Minimum-82
Maximum175530
Zeros31811
Zeros (%)4.3%
Negative2
Negative (%)< 0.1%
Memory size11.3 MiB
2025-11-22T15:52:43.206735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-82
5-th percentile2
Q141
median118
Q3410
95-th percentile6324
Maximum175530
Range175612
Interquartile range (IQR)369

Descriptive statistics

Standard deviation8330.0068
Coefficient of variation (CV)5.3097008
Kurtosis215.80612
Mean1568.8279
Median Absolute Deviation (MAD)98
Skewness13.298687
Sum1.159276 × 109
Variance69389013
MonotonicityNot monotonic
2025-11-22T15:52:43.576296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
031811
 
4.3%
125903
 
0.8%
95299
 
0.7%
24885
 
0.7%
204740
 
0.6%
34739
 
0.6%
74708
 
0.6%
14648
 
0.6%
64569
 
0.6%
114513
 
0.6%
Other values (20217)663129
89.7%
ValueCountFrequency (%)
-822
 
< 0.1%
031811
4.3%
14648
 
0.6%
24885
 
0.7%
34739
 
0.6%
42650
 
0.4%
53056
 
0.4%
64569
 
0.6%
74708
 
0.6%
84471
 
0.6%
ValueCountFrequency (%)
1755301
< 0.1%
1754741
< 0.1%
1754391
< 0.1%
1753951
< 0.1%
1753411
< 0.1%
1752891
< 0.1%
1752561
< 0.1%
1752151
< 0.1%
1751741
< 0.1%
1751101
< 0.1%

Recovered
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing738000
Missing (%)99.9%
Memory size11.3 MiB
0.0
944 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2832
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0944
 
0.1%
(Missing)738000
99.9%

Length

2025-11-22T15:52:43.721101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T15:52:43.788584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0944
100.0%

Most occurring characters

ValueCountFrequency (%)
01888
66.7%
.944
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01888
66.7%
.944
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01888
66.7%
.944
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01888
66.7%
.944
33.3%

Active
Real number (ℝ)

High correlation  Skewed 

Distinct134226
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130578.09
Minimum-4531
Maximum33422002
Zeros6454
Zeros (%)0.9%
Negative1794
Negative (%)0.2%
Memory size11.3 MiB
2025-11-22T15:52:43.873884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4531
5-th percentile521
Q13071
median8672
Q337652
95-th percentile440409
Maximum33422002
Range33426533
Interquartile range (IQR)34581

Descriptive statistics

Standard deviation810441.62
Coefficient of variation (CV)6.2065666
Kurtosis584.62648
Mean130578.09
Median Absolute Deviation (MAD)7173
Skewness20.592593
Sum9.6489896 × 1010
Variance6.5681562 × 1011
MonotonicityNot monotonic
2025-11-22T15:52:44.034186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06454
 
0.9%
13387
 
0.1%
7313
 
< 0.1%
1299
 
< 0.1%
-5295
 
< 0.1%
100291
 
< 0.1%
699280
 
< 0.1%
434276
 
< 0.1%
8257
 
< 0.1%
122239
 
< 0.1%
Other values (134216)729853
98.8%
ValueCountFrequency (%)
-45311
 
< 0.1%
-45291
 
< 0.1%
-45271
 
< 0.1%
-45173
< 0.1%
-45061
 
< 0.1%
-45052
< 0.1%
-44991
 
< 0.1%
-44773
< 0.1%
-44761
 
< 0.1%
-44741
 
< 0.1%
ValueCountFrequency (%)
334220021
 
< 0.1%
334028161
 
< 0.1%
333791451
 
< 0.1%
333435063
< 0.1%
333314211
 
< 0.1%
333116281
 
< 0.1%
332903961
 
< 0.1%
332614961
 
< 0.1%
332229103
< 0.1%
332078911
 
< 0.1%
Distinct4016
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
2025-11-22T15:52:44.439957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length45
Mean length20.426295
Min length4

Characters and Unicode

Total characters15093888
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
us603336
26.7%
texas47288
 
2.1%
virginia35144
 
1.6%
georgia29808
 
1.3%
north29440
 
1.3%
carolina27416
 
1.2%
new26680
 
1.2%
dakota22632
 
1.0%
kentucky22264
 
1.0%
south21896
 
1.0%
Other values (2758)1397480
61.7%
2025-11-22T15:52:45.085216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1524440
 
10.1%
a1382760
 
9.2%
,1308792
 
8.7%
n927544
 
6.1%
i926992
 
6.1%
o836464
 
5.5%
e834808
 
5.5%
s707664
 
4.7%
S702512
 
4.7%
r647680
 
4.3%
Other values (52)5294232
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15093888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1524440
 
10.1%
a1382760
 
9.2%
,1308792
 
8.7%
n927544
 
6.1%
i926992
 
6.1%
o836464
 
5.5%
e834808
 
5.5%
s707664
 
4.7%
S702512
 
4.7%
r647680
 
4.3%
Other values (52)5294232
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15093888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1524440
 
10.1%
a1382760
 
9.2%
,1308792
 
8.7%
n927544
 
6.1%
i926992
 
6.1%
o836464
 
5.5%
e834808
 
5.5%
s707664
 
4.7%
S702512
 
4.7%
r647680
 
4.3%
Other values (52)5294232
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15093888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1524440
 
10.1%
a1382760
 
9.2%
,1308792
 
8.7%
n927544
 
6.1%
i926992
 
6.1%
o836464
 
5.5%
e834808
 
5.5%
s707664
 
4.7%
S702512
 
4.7%
r647680
 
4.3%
Other values (52)5294232
35.1%

Incident_Rate
Real number (ℝ)

Missing 

Distinct297730
Distinct (%)41.2%
Missing17163
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean23362.236
Minimum0
Maximum1239511.5
Zeros1499
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size11.3 MiB
2025-11-22T15:52:45.292624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4591.0632
Q119679.157
median24423.797
Q328372.877
95-th percentile34957.168
Maximum1239511.5
Range1239511.5
Interquartile range (IQR)8693.7191

Descriptive statistics

Standard deviation9024.5535
Coefficient of variation (CV)0.38628809
Kurtosis471.01544
Mean23362.236
Median Absolute Deviation (MAD)4298.3536
Skewness3.7387476
Sum1.6862418 × 1010
Variance81442567
MonotonicityNot monotonic
2025-11-22T15:52:45.501892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01499
 
0.2%
1.773255814184
 
< 0.1%
186.4821515184
 
< 0.1%
12548.1111184
 
< 0.1%
12852.68405184
 
< 0.1%
13575.48508184
 
< 0.1%
12753.90455184
 
< 0.1%
9603.023339184
 
< 0.1%
13994.25746184
 
< 0.1%
16854.53728184
 
< 0.1%
Other values (297720)718626
97.3%
(Missing)17163
 
2.3%
ValueCountFrequency (%)
01499
0.2%
0.003879154259109
 
< 0.1%
0.02906976744161
 
< 0.1%
0.058139534881
 
< 0.1%
0.34883720931
 
< 0.1%
0.40697674423
 
< 0.1%
0.4511
 
< 0.1%
0.45555555562
 
< 0.1%
0.46111111111
 
< 0.1%
0.46666666671
 
< 0.1%
ValueCountFrequency (%)
1239511.4941
 
< 0.1%
343404.63462
 
< 0.1%
190532.54441
 
< 0.1%
189940.82841
 
< 0.1%
189349.11241
 
< 0.1%
188757.39643
< 0.1%
188165.68054
< 0.1%
186390.53255
< 0.1%
185798.81662
 
< 0.1%
184023.66863
< 0.1%

Case_Fatality_Ratio
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct252733
Distinct (%)34.6%
Missing7654
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.6008515
Minimum0
Maximum6200
Zeros25604
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size11.3 MiB
2025-11-22T15:52:45.692144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15698358
Q10.91806847
median1.3661972
Q31.8613861
95-th percentile2.9942879
Maximum6200
Range6200
Interquartile range (IQR)0.94331767

Descriptive statistics

Standard deviation62.783077
Coefficient of variation (CV)24.139432
Kurtosis5162.8193
Mean2.6008515
Median Absolute Deviation (MAD)0.46957271
Skewness69.54895
Sum1901976.7
Variance3941.7148
MonotonicityNot monotonic
2025-11-22T15:52:45.943501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
025604
 
3.5%
1.136363636293
 
< 0.1%
2.857142857287
 
< 0.1%
2.083333333232
 
< 0.1%
1.234567901230
 
< 0.1%
1.587301587230
 
< 0.1%
1.449275362218
 
< 0.1%
2.040816327217
 
< 0.1%
2.912621359213
 
< 0.1%
1.162790698199
 
< 0.1%
Other values (252723)703567
95.2%
(Missing)7654
 
1.0%
ValueCountFrequency (%)
025604
3.5%
0.0004240063941
 
< 0.1%
0.0029622958987
 
< 0.1%
0.0029775019957
 
< 0.1%
0.002992632147
 
< 0.1%
0.0030096006267
 
< 0.1%
0.0030271657867
 
< 0.1%
0.0030475540337
 
< 0.1%
0.0030719512427
 
< 0.1%
0.0030864578586
 
< 0.1%
ValueCountFrequency (%)
62001
< 0.1%
5848.6842111
< 0.1%
5761.251
< 0.1%
5619.7530861
< 0.1%
56001
< 0.1%
5554.2168671
< 0.1%
5541.7721521
< 0.1%
5539.743591
< 0.1%
5530.3797471
< 0.1%
5430.5882351
< 0.1%

New_Cases
Real number (ℝ)

Zeros 

Distinct167261
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.80376
Minimum-33459805
Maximum33571870
Zeros16595
Zeros (%)2.2%
Negative353831
Negative (%)47.9%
Memory size11.3 MiB
2025-11-22T15:52:46.130049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-33459805
5-th percentile-164942.25
Q1-9177
median0
Q38690
95-th percentile166812.4
Maximum33571870
Range67031675
Interquartile range (IQR)17867

Descriptive statistics

Standard deviation859090.05
Coefficient of variation (CV)6189.2419
Kurtosis785.72439
Mean138.80376
Median Absolute Deviation (MAD)9007
Skewness-0.014805279
Sum1.0256821 × 108
Variance7.3803571 × 1011
MonotonicityNot monotonic
2025-11-22T15:52:46.345660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016595
 
2.2%
1476
 
0.1%
2404
 
0.1%
13376
 
0.1%
3346
 
< 0.1%
4308
 
< 0.1%
54292
 
< 0.1%
5262
 
< 0.1%
6257
 
< 0.1%
7246
 
< 0.1%
Other values (167251)719382
97.4%
ValueCountFrequency (%)
-334598051
 
< 0.1%
-334360831
 
< 0.1%
-334003343
< 0.1%
-333882041
 
< 0.1%
-333685331
 
< 0.1%
-333472441
 
< 0.1%
-333182561
 
< 0.1%
-332795543
< 0.1%
-332644631
 
< 0.1%
-332410011
 
< 0.1%
ValueCountFrequency (%)
335718701
 
< 0.1%
335526301
 
< 0.1%
335289081
 
< 0.1%
334931593
< 0.1%
334810291
 
< 0.1%
334611921
 
< 0.1%
334399031
 
< 0.1%
334109151
 
< 0.1%
333722133
< 0.1%
333571221
 
< 0.1%

Date
Date

Distinct192
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
Minimum2020-08-04 00:00:00
Maximum2022-09-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-22T15:52:46.555205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:46.791229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-11-22T15:52:33.922369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:17.764184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:19.990347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:21.949617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:23.670424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:25.443212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:27.371855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:29.142905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:31.018806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:34.148388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:17.995387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:20.238466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:22.128049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:23.860623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:25.638330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:27.576851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:29.349474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:31.304337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:34.363059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:18.228146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:20.680709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:22.303470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:24.055135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:26.007409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:27.767963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:29.539201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:31.578256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:34.550186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:18.505631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:20.884889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:22.479270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:24.246516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:26.201862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:27.984183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:29.737209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:31.874450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:34.742065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:18.761402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:21.064038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:22.698411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:24.439560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:26.397306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:28.172863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:29.953775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:32.200090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:34.943666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:19.000433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:21.242576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:22.894806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:24.636088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:26.586956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:28.364533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:30.147209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:32.482504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:35.141228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:19.238984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:21.418975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:23.090440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:24.855723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:26.803067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:28.559941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:30.336725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:32.759536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:35.349250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:19.500398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:21.605752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:23.279968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:25.061408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:27.007574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:28.755538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:30.530501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:33.328941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:35.539966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:19.751009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:21.788239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:23.479517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:25.260275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:27.196002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:28.970715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:30.734194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T15:52:33.641454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-22T15:52:46.972927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ActiveCase_Fatality_RatioConfirmedDeathsFIPSIncident_RateLatLong_New_Cases
Active1.000-0.1971.0000.903-0.063-0.016-0.0810.4740.421
Case_Fatality_Ratio-0.1971.000-0.1930.127-0.070-0.138-0.166-0.134-0.138
Confirmed1.000-0.1931.0000.905-0.063-0.016-0.0820.4740.421
Deaths0.9030.1270.9051.000-0.126-0.044-0.1150.3900.384
FIPS-0.063-0.070-0.063-0.1261.000-0.0980.0220.134-0.015
Incident_Rate-0.016-0.138-0.016-0.044-0.0981.0000.117-0.2050.101
Lat-0.081-0.166-0.082-0.1150.0220.1171.000-0.1730.009
Long_0.474-0.1340.4740.3900.134-0.205-0.1731.0000.033
New_Cases0.421-0.1380.4210.384-0.0150.1010.0090.0331.000

Missing values

2025-11-22T15:52:35.908747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-22T15:52:36.918074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-22T15:52:38.750321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FIPSAdmin2Province_StateCountry_RegionLast_UpdateLatLong_ConfirmedDeathsRecoveredActiveCombined_KeyIncident_RateCase_Fatality_RatioNew_CasesDate
0NaNNaNNaNAfghanistan2022-03-02 04:21:0733.9391167.7099531738797609NaN166270.0Afghanistan446.6642954.3760320.02022-03-02
4016NaNNaNNaNAfghanistan2022-03-03 04:21:0133.9391167.7099531740737617NaN166456.0Afghanistan447.1626474.375750194.02022-03-03
8032NaNNaNNaNAfghanistan2022-03-04 04:21:0933.9391167.7099531742147619NaN166595.0Afghanistan447.5248514.373357141.02022-03-04
12048NaNNaNNaNAfghanistan2022-03-05 04:21:1033.9391167.7099531742147619NaN166595.0Afghanistan447.5248514.3733570.02022-03-05
16064NaNNaNNaNAfghanistan2022-03-06 04:20:4933.9391167.7099531743317622NaN166709.0Afghanistan447.8254034.372143117.02022-03-06
20080NaNNaNNaNAfghanistan2022-03-07 04:20:4733.9391167.7099531745827623NaN166959.0Afghanistan448.4701784.366430251.02022-03-07
24096NaNNaNNaNAfghanistan2022-03-08 04:20:5433.9391167.7099531750007626NaN167374.0Afghanistan449.5439454.357714418.02022-03-08
28112NaNNaNNaNAfghanistan2022-03-09 04:20:5933.9391167.7099531753537630NaN167723.0Afghanistan450.4507404.351223353.02022-03-09
32128NaNNaNNaNAfghanistan2022-03-10 04:20:4833.9391167.7099531755257636NaN167889.0Afghanistan450.8925774.350377172.02022-03-10
36144NaNNaNNaNAfghanistan2022-03-11 04:20:5033.9391167.7099531758937639NaN168254.0Afghanistan451.8379044.342981368.02022-03-11
FIPSAdmin2Province_StateCountry_RegionLast_UpdateLatLong_ConfirmedDeathsRecoveredActiveCombined_KeyIncident_RateCase_Fatality_RatioNew_CasesDate
702795NaNNaNNaNZimbabwe2022-08-23 04:20:56-19.01543829.1548572566285592NaN251036.0Zimbabwe1726.6316392.1790304.02022-08-23
706811NaNNaNNaNZimbabwe2022-08-24 04:20:46-19.01543829.1548572566285592NaN251036.0Zimbabwe1726.6316392.1790300.02022-08-24
710827NaNNaNNaNZimbabwe2022-08-25 04:21:13-19.01543829.1548572566285592NaN251036.0Zimbabwe1726.6316392.1790300.02022-08-25
714843NaNNaNNaNZimbabwe2022-08-26 04:21:02-19.01543829.1548572566755593NaN251082.0Zimbabwe1726.9478622.17902047.02022-08-26
718859NaNNaNNaNZimbabwe2022-08-27 04:20:57-19.01543829.1548572566825593NaN251089.0Zimbabwe1726.9949592.1789617.02022-08-27
722875NaNNaNNaNZimbabwe2022-08-28 04:21:02-19.01543829.1548572566825593NaN251089.0Zimbabwe1726.9949592.1789610.02022-08-28
726891NaNNaNNaNZimbabwe2022-08-29 04:21:06-19.01543829.1548572566995593NaN251106.0Zimbabwe1727.1093372.17881617.02022-08-29
730907NaNNaNNaNZimbabwe2022-08-30 04:21:03-19.01543829.1548572567045593NaN251111.0Zimbabwe1727.1429782.1787745.02022-08-30
734923NaNNaNNaNZimbabwe2022-08-31 04:20:57-19.01543829.1548572567085593NaN251115.0Zimbabwe1727.1698912.1787404.02022-08-31
738939NaNNaNNaNZimbabwe2022-09-01 04:21:03-19.01543829.1548572567265596NaN251130.0Zimbabwe1727.2909972.17975618.02022-09-01

Duplicate rows

Most frequently occurring

FIPSAdmin2Province_StateCountry_RegionLast_UpdateLatLong_ConfirmedDeathsRecoveredActiveCombined_KeyIncident_RateCase_Fatality_RatioNew_CasesDate# duplicates
295780001.0Out of ALAlabamaUS2020-12-21 13:27:30NaNNaN00NaN0.0Out of AL, Alabama, USNaNNaN0.02020-12-21184
296990001.0UnassignedAlabamaUS2020-12-21 13:27:30NaNNaN00NaN0.0Unassigned, Alabama, USNaNNaN0.02020-12-21184
303390051.0UnassignedVirginiaUS2020-12-21 13:27:30NaNNaN00NaN0.0Unassigned, Virginia, USNaNNaN0.02020-12-21184
303899999.0NaNGrand PrincessUS2020-08-04 02:27:56NaNNaN1033NaN100.0Grand Princess, USNaN2.91262154.02020-08-04184
3177NaNNaNGrand PrincessCanada2020-12-21 13:27:30NaNNaN130NaN13.0Grand Princess, CanadaNaN0.00000013.02020-12-21184
295572888.0Out of PRPuerto RicoUS2021-10-10 23:21:42NaNNaN4340NaN434.0Out of PR, Puerto Rico, USNaN0.0000000.02021-10-10183
296480023.0Out of MEMaineUS2020-08-07 22:34:20NaNNaN00NaN0.0Out of ME, Maine, USNaNNaN0.02020-08-07183
296888888.0NaNDiamond PrincessUS2020-08-04 02:27:56NaNNaN490NaN49.0Diamond Princess, USNaN0.000000-54.02020-08-04183
303590054.0UnassignedWest VirginiaUS2021-07-31 23:21:38NaNNaN00NaN0.0Unassigned, West Virginia, USNaNNaN0.02021-07-31183
3149NaNNaNDiamond PrincessCanada2020-12-21 13:27:30NaNNaN01NaN-1.0Diamond Princess, CanadaNaNNaN-13.02020-12-21183